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Model comparison

GLM-5.2 vs Qwen3.6 Plus

Data verified

Head-to-head evidence from 27 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

63.96/100
Margin
1.2pts
winning →
65.2/100
3 category wins1 category wins

Public leaderboard positions: GLM-5.2 #37 (Estimated); Qwen3.6 Plus #30 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5.2 and Qwen3.6 Plus share 27 comparable benchmark results. 4 of 8 categories are comparable. 16 results are unique to GLM-5.2; 33 to Qwen3.6 Plus.

Updated July 23, 2026
Shared results
27
GLM-5.2 only
16
Qwen3.6 Plus only
33
Comparable categories
4 / 8

Pick Qwen3.6 Plus if you want the stronger benchmark profile. GLM-5.2 only becomes the better choice if mathematics is the priority.

Confidence note. This is a partial-evidence comparison with 27 shared benchmark results across 7 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Qwen3.6 Plus has the cleaner BenchAlign overall profile here, landing at 65.2 versus 63.96. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Qwen3.6 Plus's sharpest advantage is in coding, where it averages 70.3 against 62.1. The single biggest benchmark swing on the page is HLE, 54.7% to 28.8%. GLM-5.2 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for GLM-5.2 and Qwen3.6 Plus
CategoryGLM-5.2ΔQwen3.6 Plus
MathGLM-5.295.9Margin← 35.4Qwen3.6 Plus60.5
AgenticGLM-5.281.0Margin← 19.4Qwen3.6 Plus61.6
CodingGLM-5.262.1Margin→ 8.2Qwen3.6 Plus70.3
KnowledgeGLM-5.259.6Margin← 2.5Qwen3.6 Plus57.1
ReasoningGLM-5.2Not measuredMarginNo overlapQwen3.6 Plus62.0
MultilingualGLM-5.2Not measuredMarginNo overlapQwen3.6 Plus84.7
MultimodalGLM-5.2Not measuredMarginNo overlapQwen3.6 Plus79.8
Inst. FollowingGLM-5.2Not measuredMarginNo overlapQwen3.6 Plus82.3

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · GLM-5.2B · Qwen3.6 Plus

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 28.8%
    Winner: GLM-5.2Δ 25.9
    HLE: GLM-5.2 scored 54.7%; Qwen3.6 Plus scored 28.8%. GLM-5.2 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 81%B 61.6%
    Winner: GLM-5.2Δ 19.4
    Terminal-Bench 2.0: GLM-5.2 scored 81%; Qwen3.6 Plus scored 61.6%. GLM-5.2 wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 62.1%B 56.6%
    Winner: GLM-5.2Δ 5.5
    SWE-bench Pro: GLM-5.2 scored 62.1%; Qwen3.6 Plus scored 56.6%. GLM-5.2 wins this benchmark.

  4. HMMT Feb 2026

    Math
    Source ↗
    A 92.5%B 87.8%
    Winner: GLM-5.2Δ 4.7
    HMMT Feb 2026: GLM-5.2 scored 92.5%; Qwen3.6 Plus scored 87.8%. GLM-5.2 wins this benchmark.

  5. AIME26

    Math
    Source ↗
    A 99.2%B 95.3%
    Winner: GLM-5.2Δ 3.9
    AIME26: GLM-5.2 scored 99.2%; Qwen3.6 Plus scored 95.3%. GLM-5.2 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGLM-5.2Qwen3.6 PlusComparison
Input / output priceUSD per 1M tokensGLM-5.2$1.4 input / $4.4 outputQwen3.6 PlusNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-5.2Not availableQwen3.6 PlusNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-5.2Not availableQwen3.6 PlusNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5.21MQwen3.6 Plus1MListed context windows are equal.

Benchmark Deep Dive

AgenticGLM-5.2 wins
BenchmarkGLM-5.2Qwen3.6 PlusResult
Terminal-Bench 2.0Source 81%61.6%GLM-5.2 leads
MCP AtlasSource 76.8%48.2%GLM-5.2 leads
ToolathlonSource 48.2%39.8%GLM-5.2 leads
AA Agentic IndexSource 43.1%27.6%GLM-5.2 leads
τ²-bench resultsSource 99.1%97.7%GLM-5.2 leads
GDPval-AASource 50.7%31.8%GLM-5.2 leads
GDPval-AASource 15141135GLM-5.2 leads
APEX-Agents-AASource 33.7%—Not comparable
ResearchClawBenchSource 20.7%18.0%GLM-5.2 leads
AA BriefcaseSource 1260—Not comparable
AA AutomationBenchSource 27.8%—Not comparable
AA EnterpriseOps-GymSource 42.7%—Not comparable
AA Harvey LABSource 91.0%—Not comparable
AA ITBenchSource 42.7%—Not comparable
AA Tau3 BankingSource 26.8%—Not comparable
terminalBenchHardSource 50.8%—Not comparable
aaTerminalBench21Source 77.9%—Not comparable
Claw-EvalSource —58.8%Not comparable
QwenClawBenchSource —57.2%Not comparable
τ³-bench resultsSource —70.7%Not comparable
VITA-BenchSource —44.3%Not comparable
DeepPlanningSource —41.5%Not comparable
MCP-TasksSource —74.1%Not comparable
WideResearchSource —74.3%Not comparable
Gert LabsSource —50.60%Not comparable
CodingQwen3.6 Plus wins
BenchmarkGLM-5.2Qwen3.6 PlusResult
SWE-bench ProSource 62.1%56.6%GLM-5.2 leads
NL2RepoSource 48.9%—Not comparable
Terminal-Bench 2.0Source 81.0%—Not comparable
ProgramBenchSource 63.7%—Not comparable
cursorBench32Source 55.0%—Not comparable
AA Coding IndexSource 68.8%54.5%GLM-5.2 leads
AA-SciCodeSource 50.5%40.7%GLM-5.2 leads
SWE-bench VerifiedSource —78.8%Not comparable
SWE MultilingualSource —73.8%Not comparable
LiveCodeBench v6Source —87.1%Not comparable
Vibe Code BenchSource —25.56%Not comparable
Reasoning
BenchmarkGLM-5.2Qwen3.6 PlusResult
CritPtSource 20.9%2.9%GLM-5.2 leads
AA-LCRSource 71.3%69.7%GLM-5.2 leads
AI-NeedleSource —68.3%Not comparable
LongBench v2Source —62%Not comparable
KnowledgeGLM-5.2 wins
BenchmarkGLM-5.2Qwen3.6 PlusResult
GPQASource 91.2%90.4%GLM-5.2 leads
GPQA-DSource 91.2%—Not comparable
HLESource 54.7%28.8%GLM-5.2 leads
HLE w/o toolsSource 40.5%—Not comparable
Artificial Analysis Intelligence IndexSource 51.1%39.6%GLM-5.2 leads
AA-GPQA DiamondSource 89.5%88.2%GLM-5.2 leads
AA-HLESource 40.1%25.7%GLM-5.2 leads
AA-Omniscience IndexSource 4.0%2.7%GLM-5.2 leads
AA-Omniscience AccuracySource 25.1%26.2%Qwen3.6 Plus leads
AA-Omniscience Hallucination RateSource 28.1%32.0%GLM-5.2 leads
AA Openness IndexSource 44.4%—Not comparable
SuperGPQASource —71.6%Not comparable
MMLU-ProSource —88.5%Not comparable
MMLU-ReduxSource —94.5%Not comparable
C-EvalSource —93.3%Not comparable
MathGLM-5.2 wins
BenchmarkGLM-5.2Qwen3.6 PlusResult
AIME26Source 99.2%95.3%GLM-5.2 leads
HMMT Nov 2025Source 94.4%94.6%Qwen3.6 Plus leads
HMMT Feb 2026Source 92.5%87.8%GLM-5.2 leads
MMAnswerBenchSource 91.0%83.8%GLM-5.2 leads
HMMT Feb 2025Source —96.7%Not comparable
FrontierMath v2 (Tiers 1-3)Source —26.207%Not comparable
FrontierMath v2 (Tier 4)Source —8.333%Not comparable
Multilingual
BenchmarkGLM-5.2Qwen3.6 PlusResult
MMLU-ProXSource —84.7%Not comparable
NOVA-63Source —57.9%Not comparable
Multimodal
BenchmarkGLM-5.2Qwen3.6 PlusResult
Design Arena WebsiteSource 13401249GLM-5.2 leads
MMMUSource —86.0%Not comparable
MMMU-ProSource —78.8%Not comparable
MathVisionSource —88.0%Not comparable
VideoMMMUSource —84.0%Not comparable
ScreenSpot ProSource —68.2%Not comparable
CharXivSource —81.5%Not comparable
V*Source —96.9%Not comparable
AA-MMMU-ProSource —78.0%Not comparable
Inst. Following
BenchmarkGLM-5.2Qwen3.6 PlusResult
AA-IFBenchSource 73.3%75.2%Qwen3.6 Plus leads
IFEvalSource —94.3%Not comparable
IFBenchSource —75.8%Not comparable
Frequently Asked Questions (5)

Which is better, GLM-5.2 or Qwen3.6 Plus?

Qwen3.6 Plus is ahead on BenchLM's BenchAlign leaderboard, 65.2 to 63.96. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 28.8%.

Which is better for knowledge tasks, GLM-5.2 or Qwen3.6 Plus?

GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 57.1. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5.2 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for coding in this comparison, averaging 70.3 versus 62.1. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5.2 or Qwen3.6 Plus?

GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 60.5. Inside this category, MMAnswerBench is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5.2 or Qwen3.6 Plus?

GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 61.6. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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